| Summary: | Metabolism expresses the phenotype of living cells and understanding
it is crucial for di_erent applications in biotechnology and health. However,
metabolism involves an intricate network of biochemical reactions and
the measure of their activity requires sophisticated methods that integrate
disparate experimental data.
High-throughput 'omics' technologies have posed a novel scenario where
metabolism can be more globally analyzed and have brought about a
new _eld of research termed metabolic systems biology. Speci_cally,
organism-speci_c genome-scale metabolic networks, which de_ne the
speci_c set of reactions for a given organism based on its genomic data,
constitute the core of metabolic systems biology. The use of genome-scale
metabolic networks is becoming more and more popular and their value has
been proved for di_erent applications.
In particular, these networks allow the analysis of metabolic pathways
at an unprecedented level of detail and, to that end, di_erent mathematical
pathway concepts have been developed. The concept of elementary
_ux modes (EFMs) holds a prominent place in the _eld of metabolic systems
biology, as it goes beyond prede_ned pathways and correctly captures
_exibility found in metabolic systems.
With the increasing availability of metabolomic, proteomic and, to a
larger extent, transcriptomic data, the elucidation of speci_c metabolic
properties in di_erent scenarios and cell types is a key topic in systems
biology. The use of EFMs for this purpose has been limited so far, mainly
because their computation has been infeasible for genome-scale metabolic
networks.
The purpose of this doctoral thesis is to overcome these issues and develop
a novel framework for contextualizing gene expression data based on
EFMs arising from genome-scale metabolic networks. Speci_cally, this thesis
focuses on:
The computation of a representative subset of EFMs that characterize
global metabolic properties for a given cell/organism.
A general statistical framework for selecting the most relevant EFMs
in di_erent scenarios based on gene expression data.
Application of this framework to distinguish metabolic features between
various lung cancer subtypes.
Dissertation structure
This doctoral thesis is structured in two main parts. The _rst part contains
the dissertation, including the state of the art and the main contributions
made. The second part includes a complete copy of the publications
that came out of the development of this thesis. In particular, the doctoral
thesis is divided into the following chapters:
Part I: Dissertation
1. Preliminaries.
2. EFMs computation in genome-scale metabolic networks.
3. Integration of gene expression data into EFMs.
4. Conclusions and future work.
Part II: Publications
Appendix A: Paper 1
Appendix B: Paper 2
Appendix C: Paper 3
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